Next-Stage AI Entity Graph Schema Explained

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This schema transforms ThatWare’s website from a collection of pages and files into a structured, machine-readable intelligence system.

Its primary purpose is to enable advanced systems—such as search engines, AI crawlers, large language models (LLMs), answer engines, generative search platforms, RAG pipelines, knowledge graphs, and semantic search systems—to interpret ThatWare as a cohesive, interconnected entity rather than a set of isolated URLs.

Comprehensive Explanation of the Next-Stage AI Entity Graph Schema for AI Index

Core Objective of the Schema

The objective is to create one unified AI-readable graph where every important file, concept, dataset, endpoint, and trust layer connects back to the same organization entity:

Home

This creates one canonical identity for ThatWare.

The schema helps machines understand:

Who ThatWare is
What ThatWare specializes in
Which AI files belong to ThatWare
Which files are datasets
Which files are instructional documents
Which concepts ThatWare own or define
Which files support trust, reasoning, retrieval, and context
Which endpoints may AI systems use
Which signals are fresh or recently updated

The Master Entity: Organization

Root Entity

The root node of the schema is:

Home

This node represents ThatWare LLP and serves as the central entity of the entire schema.

It is defined using the Organization type, as ThatWare is a business entity.

Purpose

This section communicates to machines that:

ThatWare LLP is the primary organization
All AI, SEO, AEO, GEO, and semantic intelligence assets are associated with this entity

Key Properties

The root node includes the following important properties:

name
alternateName
url
description
sameAs
knowsAbout
subjectOf

Why This Matters

A critical aspect of this schema is the use of a stable @id.

Instead of referencing “ThatWare” inconsistently across multiple locations, every file points to a single identifier:

Home

This approach significantly improves:

Entity consistency
Brand disambiguation
Knowledge graph clarity
LLM comprehension
Search engine confidence
AI citation accuracy

This structure ensures ThatWare is understood as a unified, authoritative entity across all machine-driven systems.

DefinedTermSet: Concept Dictionary

The schema defines a structured term set:

https://thatware.co/ai-manifesto.json#term-set

This term set is implemented as a DefinedTermSet, functioning as a controlled vocabulary for ThatWare’s AI and SEO concepts. It includes key entities such as AIEO, Quantum SEO, GEO, AEO, and Semantic SEO.

Purpose

The objective is to establish that these are not arbitrary keywords, but formally defined concepts within ThatWare’s AI optimization ecosystem. This structure enables machines to clearly interpret:

What each concept represents
Where each concept is formally defined
Which resources reference or apply these concepts
How these concepts relate to the broader organizational framework

Benefit

This approach significantly enhances LLM optimization. AI systems perform more effectively when concepts are explicitly named, well-defined, and interconnected—improving semantic clarity, contextual understanding, and knowledge retrieval.

DefinedTerm: AIEO

Definition

AIEO stands for Artificial Intelligence Experience Optimization—a proprietary concept associated with ThatWare.

Purpose

AIEO establishes a distinct conceptual signal for AI systems, identifying it as a unique framework tied to ThatWare.

It integrates with structured data sources such as:

context-engine.json
ai-signals.json

Benefits

Implementing AIEO strengthens how large language models (LLMs) and AI-driven answer engines recognize and associate ThatWare with AI experience optimization. This leads to improvements in:

Topical Authority – Reinforces subject-matter expertise
Entity–Topic Association – Strengthens linkage between ThatWare and the concept
AI Answer Inclusion – Increases likelihood of being referenced in AI-generated responses
Semantic Relevance – Enhances contextual understanding
Concept Ownership – Establishes ThatWare as the originator of AIEO

DefinedTerm: Quantum SEO

Quantum SEO is an advanced search optimization methodology that leverages predictive modeling, semantic analysis, probabilistic frameworks, and AI-assisted ranking systems.

Purpose

This defines Quantum SEO as a distinct conceptual node within the system.

It establishes structured connections with:

reasoning-map.json
knowledge-graph.json

Benefit

This strengthens ThatWare’s positioning in AI-driven SEO innovation.
It also enables machines to recognize Quantum SEO as a core component of ThatWare’s specialized knowledge architecture.

DefinedTerm: GEO

Purpose

GEO defines how ThatWare is understood and represented within AI-driven search systems. It establishes a structured relationship between the brand and generative search visibility.

It connects with:

trust-signals.json
citation-preferences.json

Benefit

GEO focuses on ensuring ThatWare appears accurately and consistently in AI-generated responses.

This schema supports GEO by providing AI systems with:

Clear concept definitions
Verified trust signals
Structured citation preferences
Strong entity associations
Contextual relevance

DefinedTerm: AEO

Purpose

AEO defines ThatWare’s approach to optimizing content for modern answer engines.
It works in alignment with structured frameworks such as:

ai-signals.json
trust-signals.json

Why It Matters

Answer engines prioritize content that is structured, credible, and ready for direct extraction. AEO ensures your information meets these requirements.

Key Benefits

Improves eligibility for direct answers
Enhances clarity in featured responses
Strengthens understanding in AI-generated overviews
Increases citation confidence
Optimizes content for accurate answer extraction

DefinedTerm: Semantic SEO

Semantic SEO focuses on enhancing search understanding by structuring content around entities, topics, meaning, and their relationships.

Purpose

This concept is connected to:

knowledge-graph.json
context-engine.json

Benefit

Semantic SEO strengthens how machines interpret and organize meaning across ThatWare’s content and AI systems.

It supports:

Entity-based SEO
Topic clustering
Knowledge graph development
Improved semantic search visibility
More accurate contextual AI responses

Master AI Index

Primary File: https://thatware.co/ai-index.json

Schema Used: DataCatalog

Purpose

The Master AI Index serves as the central entry point for all AI systems and crawlers interacting with ThatWare’s ecosystem.

It provides a clear directive: start here.

This file acts as the complete map of ThatWare’s AI-readable infrastructure, enabling structured discovery and efficient navigation across all core datasets.

Core Datasets Included

rag-index.json
reasoning-map.json
context-engine.json
knowledge-graph.json
entity-authority.json
ai-signals.json
trust-signals.json
citation-preferences.json
ai-endpoints.json
activity-stream.json

Connected Supporting Files

ai-manifesto.json
llms.txt
llms-full.txt
ai.txt
vector-feed.xml
semantic-sitemap.xml

Benefits

The Master AI Index establishes a single, authoritative entry point for AI systems.

Instead of relying on fragmented or random discovery, it provides a structured, machine-readable map, resulting in:

Improved AI crawlability
More efficient LLM ingestion
Enhanced semantic discovery
Better dataset organization
Stronger knowledge graph coherence
Increased entity trust and confidence

AI Manifesto

The AI Manifesto is a CreativeWork that serves as both a policy framework and a conceptual guide.

Purpose

It defines and explains ThatWare’s core philosophy around:

AI-driven search
AEO (Answer Engine Optimization)
GEO (Generative Engine Optimization)
Semantic SEO
Entity authority
Reasoning systems

It establishes how these elements work together to shape ThatWare’s AI understanding.

Connected Components

The AI Manifesto is linked to key system files and structures, including:

reasoning-map.json
context-engine.json
ai-signals.json
trust-signals.json
DefinedTermSet

These connections ensure alignment between conceptual intent and technical implementation.

Benefit

The manifesto provides AI systems with a structured, high-level interpretation of the entire ecosystem.

It enables clear answers to critical questions such as:

How should AI systems understand ThatWare’s brand?
Which concepts are most important?
Which files define AI logic and reasoning?
Which components establish trust, authority, and credibility?

Reasoning Map

The Reasoning Map is a structured dataset that defines how ThatWare’s AI interprets and processes information.

Purpose

It outlines the internal logic behind the AI interpretation workflow and provides machine-readable guidance using additionalProperty.

Key elements include:

Inference Input: User query
Inference Process: Entity matching, intent scoring, context layering, trust validation
Inference Output: Ranked response

Dependencies:

Used by: rag-index.json
Depends on: knowledge-graph.json
Confidence Score: 0.97

Why This Matters

The Reasoning Map acts as the AI’s “thinking framework.”

It ensures that every response is generated through a consistent, structured reasoning pipeline:

Entity matching
Intent scoring
Context layering
Trust validation

Benefits

This framework enables:

More accurate AI reasoning
Improved semantic interpretation
Reduced hallucination
Stronger RAG (Retrieval-Augmented Generation) logic
Higher-quality responses
More reliable AI outputs

RAG Index

The RAG Index is a structured retrieval dataset designed to power efficient information access within AI systems.

Purpose

It enables AI models to accurately retrieve relevant ThatWare knowledge by organizing and linking critical data sources.

Connected Components

The RAG Index integrates with:

reasoning-map.json
knowledge-graph.json
context-engine.json

Key Functionality

RAG systems rely on well-defined retrieval pathways. The RAG Index provides a clear map that helps AI systems understand:

Where to retrieve specific information
Which files contain contextual data
Which sources define entity relationships
Which components support reasoning processes

Benefits

By structuring retrieval effectively, the RAG Index significantly enhances:

Retrieval accuracy
LLM grounding
Answer relevance
Reduction of hallucinations
Quality of AI-generated summaries

Context Engine

The Context Engine is a structured dataset designed for advanced contextual interpretation.

Purpose

It establishes meaningful connections between entities, concepts, signals, and semantic relationships to enable accurate understanding by AI systems.

Core References

The engine integrates and maps key elements, including:

ThatWare organization
AIEO (AI Engine Optimization)
Semantic SEO
RAG Index
Reasoning Map
AI Signals

Value Proposition

This dataset enhances how AI systems interpret and process content by providing clear contextual grounding.

For example, when an AI encounters the term “GEO,” the Context Engine ensures it is correctly understood as:

Generative Engine Optimization

rather than a geographic reference.

Impact

By resolving ambiguity and enriching context, the Context Engine significantly improves:

Entity disambiguation
Contextual accuracy
Semantic classification
Topic comprehension
LLM response precision

Knowledge Graph

The Knowledge Graph dataset represents ThatWare’s structured network of entities, concepts, and their relationships within its AI ecosystem.

Purpose

It establishes meaningful connections between key components such as:

ThatWare, AIEO, Quantum SEO, GEO, AEO, Semantic SEO, Entity Authority, RAG Index, and the Reasoning Map.

Value & Functionality

This dataset transforms ThatWare’s ecosystem into an interconnected semantic graph, enabling machines to clearly interpret:

Which concepts are owned or defined by ThatWare
How concepts are linked to specific files and data sources
How different files reinforce and support each other
Which datasets contribute to authority, retrieval, and reasoning

Impact

By structuring knowledge in this way, the Knowledge Graph enhances:

Knowledge graph visibility across systems
Entity-based SEO performance
Semantic search understanding
Concept and relationship mapping
Accuracy and depth of AI-generated explanations

Entity Authority

Overview

Entity Authority is a structured dataset designed to strengthen brand validation and recognition.

Purpose

It defines and reinforces ThatWare’s identity by establishing its authority, credibility, and trusted external associations. This includes linking to verified sources such as:

sameAs references
Clutch profile
Forbes mentions

Benefits

This dataset enhances machine understanding and confidence in the entity by enabling:

Accurate brand verification
Improved entity resolution
Stronger authority validation
Knowledge graph consolidation
Reliable external trust associations

For large language models (LLMs), Entity Authority reduces ambiguity and prevents confusion between ThatWare and similarly named entities or organizations.

AI Signals

Overview

AI Signals is a structured dataset designed to provide machine-readable optimization signals that help AI systems better understand and evaluate ThatWare’s relevance across digital ecosystems.

Purpose

It establishes ThatWare’s authority and contextual relevance in key areas such as:

Search ranking
Entity resolution
Semantic SEO
Answer Engine Optimization (AEO)
Generative Engine Optimization (GEO)
AI-driven interpretation

System Integration

AI Signals works in conjunction with:

Entity Authority frameworks
Trust Signal mechanisms
Context Engine infrastructure
AI Entity Optimization (AIEO)
AEO and GEO systems

Benefits

By supplying structured, machine-readable cues, AI Signals enables AI systems to more accurately identify and position ThatWare within relevant contexts.

This leads to improvements in:

AI ranking confidence
Answer engine visibility and relevance
Topic association accuracy
Large Language Model (LLM) interpretation
Semantic matching and contextual alignment

Trust Signals

Overview

Trust Signals represent one of the most critical layers in the system, directly influencing whether AI platforms choose to cite, recommend, or prioritize a source.

Purpose

This layer establishes credibility through structured signals such as validation, authority, and verifiable citations. It incorporates AI-readable metadata, including:

Trust Source: ThatWare Research
Validation Method: Multi-model consensus
Recommended For: Answer engine trust, citation confidence, AI ranking confidence
Confidence Score: 0.97

Why It Matters

Modern AI systems rely on trust signals to evaluate the reliability of information before surfacing it in responses. Without these signals, even high-quality content may be ignored.

Key Benefits

This layer strengthens:

AI citation confidence
Answer engine trustworthiness
GEO (Generative Engine Optimization) visibility
AEO (Answer Engine Optimization) authority
Brand credibility
Reduced hallucination risk

Impact

By embedding robust trust signals, content becomes significantly more eligible for AI-driven discovery, citation, and recommendation—positioning it as a reliable source within automated knowledge ecosystems.

Citation Preferences

Citation Preferences define how AI systems should handle attribution and source selection for ThatWare.

Purpose

This aligns with key elements such as:

Trust Signals
Entity Authority

Benefits

Implementing Citation Preferences enhances:

Consistency in AI-generated citations
Accurate source attribution
Inclusion in answer engine references
Brand mention accuracy
Visibility in generative AI responses

Why It Matters

Citation Preferences are particularly valuable for GEO (Generative Engine Optimization), as AI-generated answers heavily depend on how sources are selected and cited.

AI Endpoints

AI Endpoints define callable or conceptual API-style interfaces designed for structured AI interaction.

They are built using EntryPoint objects such as:

entity-lookup
context-lookup
trust-lookup

Purpose

AI Endpoints signal that ThatWare provides structured access layers for:

Entity lookup
Context retrieval
Trust evaluation

Benefits

Even when implemented as lightweight or forward-looking constructs, these endpoints establish a clear, machine-readable interface model. This enables:

Enhanced AI agent discoverability
Standardized machine-readable access
Structured query interpretation
Future-ready API architecture
Scalable entity intelligence systems

Activity Stream

The Activity Stream acts as the system’s freshness layer, capturing real-time evolution.

Purpose

It records updates, changes, and the progression of concepts over time.

Key Information Includes:

Latest update timestamp
Updated entity
Nature of the change
Impact of the change
Confidence score

Why It Matters

Freshness is critical for both search engines and AI systems. This layer signals that:

The system is actively maintained
Entities are continuously evolving
AI logic is being refined
Content remains current, not stale
Trust signals are consistently reinforced

Impact & Benefits

Improved crawl prioritization
Stronger freshness confidence
Increased AI system trust
More relevant generative responses

llms.txt

llms.txt is a machine-readable file designed specifically for large language models.

Purpose

It defines how LLMs should access, interpret, and navigate content.

Benefits

It enables:

Efficient LLM crawling
Improved AI content understanding
Preferred source discovery
Accurate entity interpretation
Reduced hallucination

llms-full.txt

llms-full.txt is an expanded reference file for LLMs.

Purpose

It provides comprehensive details, structured instructions, and extended content references.

Benefits

It allows LLMs to understand ThatWare more deeply by supporting:

Detailed AI ingestion
More accurate brand representation
Stronger factual grounding
Context-rich AI responses

ai.txt

ai.txt is a concise instruction file for AI systems.

Purpose

It defines how AI should interpret ThatWare’s structured data and entity relationships.

Benefits

It ensures:

Controlled AI interpretation
Consistent entity understanding
Preferred source routing
Clear guidance for AI crawlers

Vector Feed

The Vector Feed integrates with embedding and retrieval systems.

Purpose

It powers semantic search, vector-based retrieval, and RAG (Retrieval-Augmented Generation) workflows.

Benefits

It supports:

Embedding systems
Semantic similarity matching
Vector search operations
RAG pipelines
Enhanced LLM answer generation

Semantic Sitemap

A semantic sitemap connects pages and AI-related files based on meaning—not just URL hierarchy.

Purpose

It enables machines to understand relationships between content across the website.

Benefits

This enhances:

Semantic crawling
Topic discovery
Knowledge graph mapping
Entity-first navigation
AI-readable site architecture

Why additionalProperty Was Used

Custom fields such as aiUsage, priority, confidence, usedBy, dependsOn, inference, and trust are not part of the standard Schema.org vocabulary.

Using them directly results in validation errors. To maintain compliance, they were mapped to:

additionalProperty → PropertyValue

Example

{

“@type”: “PropertyValue”,

“name”: “Confidence score”,

“value”: “0.98”

}

Benefit

This approach preserves schema validity while embedding AI-specific metadata, giving LLMs and agents richer context for interpretation.

Why DefinedTermSet Was Added

Previously, inDefinedTermSet incorrectly referenced a CreativeWork, which caused structural issues.

This was resolved by introducing a proper DefinedTermSet, with each concept linked to it.

Benefits

Establishes a structured concept dictionary
Improves schema correctness
Ensures consistent terminology
Enhances machine-readable definitions
Strengthens LLM concept mapping and semantic SEO

Why This Schema Improves LLM Optimization

LLM optimization focuses on making your content, brand, and entities easier for AI systems to understand, retrieve, and cite.

This schema supports that by providing:

Clear entity identity
Stable organization identifiers
Well-defined concepts
AI-readable file relationships
Reasoning metadata
Trust signals
Citation preferences
Content freshness indicators
Endpoint discovery
Retrieval index mapping

LLM Benefits

Stronger brand comprehension
Improved entity recognition
Higher-quality answer generation
Reduced hallucination risk
Better source attribution
More accurate summaries
Optimized retrieval pathways
Increased likelihood of inclusion in AI-generated responses

Why This Schema Supports AEO

AEO (Answer Engine Optimization) focuses on making content easily extractable for answer engines.

Answer engines prioritize information that is clean, structured, and trustworthy.

This schema supports AEO by providing:

Clear entity definitions
Structured topic relationships
Built-in trust validation
Defined citation preferences
Strong contextual meaning
Answer-ready concept formatting

AEO Benefits

Improved answer extraction
Better AI Overview interpretation
Increased direct-answer eligibility
Higher source trust signals
More accurate brand mentions
Stronger topical relevance

Why This Schema Supports GEO

GEO (Generative Engine Optimization) ensures your brand is usable within AI-generated responses.

Generative engines synthesize answers from multiple sources—so inclusion depends on clarity, trust, and contextual alignment.

This schema enables GEO through:

Machine-readable brand identity
Structured concept definitions
Layered trust and authority signals
Freshness indicators
Citation guidance
Embedded reasoning metadata
Defined retrieval pathways

GEO Benefits

Greater inclusion in AI-generated answers
Higher likelihood of citation
Stronger entity association
Improved trust perception
More accurate generative summaries
Consistent brand representation across AI systems

Why This Strengthens Entity SEO

Entity SEO focuses on how systems understand real-world entities and their relationships.

This schema builds a unified entity framework with:

A single organization identity
Multiple structured concept entities
Interconnected datasets
External references and authority links
Embedded trust signals
Knowledge graph compatibility

Entity SEO Benefits

Stronger entity consolidation
Reduced ambiguity
Improved brand recognition
Better knowledge graph integration
Clearer topical identity
Enhanced relationship mapping

Why This Enhances Semantic SEO

Semantic SEO is about meaning, context, and relationships between concepts.

This schema connects:

Organization data
Concept layers
AI-specific files
Trust and authority signals
Reasoning frameworks
Retrieval systems
Knowledge graph structures
Activity and update streams

Semantic SEO Benefits

Improved topic clustering
Stronger context understanding
Better content interpretation
Deeper concept relationships
Enhanced machine comprehension
Increased search relevance

Final Architecture

The complete AI architecture is structured as a multi-layered intelligence framework:

ThatWare LLP

Home

↓

Defined AI Concepts

AIEO | Quantum SEO | GEO | AEO | Semantic SEO

↓

Master AI Index

https://thatware.co/ai-index.json

↓

Core Intelligence Datasets

Reasoning Map
RAG Index
Context Engine
Knowledge Graph
Entity Authority
AI Signals
Trust Signals
Citation Preferences
AI Endpoints
Activity Stream

↓

Supporting AI Files

AI Manifesto
llms.txt
llms-full.txt
ai.txt
vector-feed.xml
semantic-sitemap.xml

This layered system transforms the website into a structured, interoperable AI ecosystem rather than a traditional web property.

Strategic Outcome

The result is not just a website —

it is a machine-readable intelligence layer.

This architecture establishes ThatWare as an AI-first digital entity, built for the evolving landscape of search and discovery.

Modern search is no longer limited to pages and keywords. It is driven by:

Entities
Trust & authority
Contextual relevance
Retrieval systems (RAG)
Reasoning capability
Citation integrity
Content freshness
AI interpretation layers
Machine-readable knowledge structures

Final Summary

This framework creates a robust foundation for:

LLM Optimization
Answer Engine Optimization (AEO)
Generative Engine Optimization (GEO)
Entity SEO
Semantic SEO
AI crawler discoverability
Knowledge graph alignment
RAG system compatibility
Answer engine trust signals
Inclusion in generative responses

Key Advantages

Unified entity identity across all AI layers
Centralized AI index for structured discoverability
Clearly defined conceptual architecture
Strong cross-file semantic interlinking
Embedded machine-readable reasoning signals
Multi-layer trust and authority framework
Real-time freshness via activity streams
Dedicated AI endpoint infrastructure
Validator-safe, standards-compliant implementation
Optimized for LLM parsing and interpretation

Positioning Statement

ThatWare is no longer just a website.

It is a search-native AI intelligence system designed for:

Machines to understand
Models to retrieve
Engines to trust
And AI systems to cite

Transition

Below is the practical schema implementation for this AI knowledge index layer:

<script type=”application/ld+json”>

{

“@context”: “https://schema.org”,

“@graph”: [

{

“@type”: “Organization”,

“@id”: “https://thatware.co/#organization”,

“name”: “ThatWare LLP”,

“alternateName”: “ThatWare”,

“url”: “https://thatware.co/”,

“description”: “ThatWare LLP is an AI SEO, AEO, GEO and semantic search optimization company focused on entity intelligence, search automation and machine-readable optimization systems.”,

“sameAs”: [

“https://www.clutch.co/profile/thatware”,

“https://www.forbes.com/”

“knowsAbout”: [

{ “@id”: “https://thatware.co/#aieo” },

{ “@id”: “https://thatware.co/#qseo” },

{ “@id”: “https://thatware.co/#geo” },

{ “@id”: “https://thatware.co/#aeo” },

{ “@id”: “https://thatware.co/#semantic-seo” }

“subjectOf”: [

{ “@id”: “https://thatware.co/ai-index.json” },

{ “@id”: “https://thatware.co/ai-manifesto.json” },

{ “@id”: “https://thatware.co/reasoning-map.json” },

{ “@id”: “https://thatware.co/entity-authority.json” }

]

{

“@type”: “DefinedTermSet”,

“@id”: “https://thatware.co/ai-manifesto.json#term-set”,

“name”: “ThatWare AI Optimization Terms”,

“description”: “A defined term set describing ThatWare’s AI SEO, AEO, GEO, semantic SEO and machine-readable optimization concepts.”,

“url”: “https://thatware.co/ai-manifesto.json”,

“creator”: { “@id”: “https://thatware.co/#organization” },

“publisher”: { “@id”: “https://thatware.co/#organization” },

“hasDefinedTerm”: [

{ “@id”: “https://thatware.co/#aieo” },

{ “@id”: “https://thatware.co/#qseo” },

{ “@id”: “https://thatware.co/#geo” },

{ “@id”: “https://thatware.co/#aeo” },

{ “@id”: “https://thatware.co/#semantic-seo” }

]

{

“@type”: “DefinedTerm”,

“@id”: “https://thatware.co/#aieo”,

“name”: “Artificial Intelligence Experience Optimization”,

“description”: “AIEO represents the optimization of digital experiences for AI systems, answer engines, generative engines and machine interpretation.”,

“inDefinedTermSet”: { “@id”: “https://thatware.co/ai-manifesto.json#term-set” },

“subjectOf”: [

{ “@id”: “https://thatware.co/context-engine.json” },

{ “@id”: “https://thatware.co/ai-signals.json” }

]

{

“@type”: “DefinedTerm”,

“@id”: “https://thatware.co/#qseo”,

“name”: “Quantum SEO”,

“description”: “Quantum SEO represents advanced search optimization using predictive, semantic, probabilistic and AI-assisted ranking methodologies.”,

“inDefinedTermSet”: { “@id”: “https://thatware.co/ai-manifesto.json#term-set” },

“subjectOf”: [

{ “@id”: “https://thatware.co/reasoning-map.json” },

{ “@id”: “https://thatware.co/knowledge-graph.json” }

]

{

“@type”: “DefinedTerm”,

“@id”: “https://thatware.co/#geo”,

“name”: “Generative Engine Optimization”,

“description”: “GEO is the process of optimizing entities, content and authority signals for visibility inside AI-generated answers.”,

“inDefinedTermSet”: { “@id”: “https://thatware.co/ai-manifesto.json#term-set” },

“subjectOf”: [

{ “@id”: “https://thatware.co/trust-signals.json” },

{ “@id”: “https://thatware.co/citation-preferences.json” }

]

{

“@type”: “DefinedTerm”,

“@id”: “https://thatware.co/#aeo”,

“name”: “Answer Engine Optimization”,

“description”: “AEO is the process of structuring content, entities and citations so answer engines can extract and present accurate responses.”,

“inDefinedTermSet”: { “@id”: “https://thatware.co/ai-manifesto.json#term-set” },

“subjectOf”: [

{ “@id”: “https://thatware.co/ai-signals.json” },

{ “@id”: “https://thatware.co/trust-signals.json” }

]

{

“@type”: “DefinedTerm”,

“@id”: “https://thatware.co/#semantic-seo”,

“name”: “Semantic SEO”,

“description”: “Semantic SEO improves search understanding by connecting topics, entities, context and structured meaning across a website.”,

“inDefinedTermSet”: { “@id”: “https://thatware.co/ai-manifesto.json#term-set” },

“subjectOf”: [

{ “@id”: “https://thatware.co/knowledge-graph.json” },

{ “@id”: “https://thatware.co/context-engine.json” }

]

{

“@type”: “DataCatalog”,

“@id”: “https://thatware.co/ai-index.json”,

“name”: “ThatWare Master AI Index”,

“description”: “The central AI entry point that maps ThatWare’s organization entity, AI concepts, reasoning files, retrieval indexes, trust signals, endpoints and activity layers.”,

“url”: “https://thatware.co/ai-index.json”,

“creator”: { “@id”: “https://thatware.co/#organization” },

“publisher”: { “@id”: “https://thatware.co/#organization” },

“about”: { “@id”: “https://thatware.co/#organization” },

“keywords”: [

“AI SEO”,

“AEO”,

“GEO”,

“LLM optimization”,

“semantic SEO”,

“entity optimization”,

“machine-readable intelligence”

“dataset”: [

{ “@id”: “https://thatware.co/rag-index.json” },

{ “@id”: “https://thatware.co/reasoning-map.json” },

{ “@id”: “https://thatware.co/context-engine.json” },

{ “@id”: “https://thatware.co/knowledge-graph.json” },

{ “@id”: “https://thatware.co/entity-authority.json” },

{ “@id”: “https://thatware.co/ai-signals.json” },

{ “@id”: “https://thatware.co/trust-signals.json” },

{ “@id”: “https://thatware.co/citation-preferences.json” },

{ “@id”: “https://thatware.co/ai-endpoints.json” },

{ “@id”: “https://thatware.co/activity-stream.json” }

“hasPart”: [

{ “@id”: “https://thatware.co/ai-manifesto.json” },

{ “@id”: “https://thatware.co/llms.txt” },

{ “@id”: “https://thatware.co/llms-full.txt” },

{ “@id”: “https://thatware.co/ai.txt” },

{ “@id”: “https://thatware.co/vector-feed.xml” },

{ “@id”: “https://thatware.co/semantic-sitemap.xml” }

“additionalProperty”: [

{

“@type”: “PropertyValue”,

“name”: “AI usage priority”,

“value”: “high”

{

“@type”: “PropertyValue”,

“name”: “Recommended AI usage”,

“value”: “entity resolution, semantic SEO, answer generation, generative engine optimization, ranking interpretation”

{

“@type”: “PropertyValue”,

“name”: “Confidence score”,

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